The landmark 1999 Golub, Slonim et al study represented the first demonstration that genomic approaches (in this case gene expression profiling) could be used to identify new cancer subtypes or assign tumors to known classes. Here, you can download the original proof-of-concept data demonstrating successful classification between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes.

The Cancer Cell Line Encyclopedia (CCLE) project is an effort to conduct a detailed genetic characterization of a large panel of human cancer cell lines. The CCLE provides public access analysis and visualization of DNA copy number, mRNA expression, mutation data and more, for 1000 cancer cell lines.

The limited availability of appropriate disease models is a major bottleneck that limits research impact. If model systems representing the genetic diversity of diseases such as cancer were readily available, experiments to understand the biological consequence of particular gene mutations and to develop new therapeutic strategies could proceed more rapidly. Until recently, despite enormous effort dedicated to the creation of cancer cell line models, success rates have been very low (often zero).
We have launched a pilot Cancer Cell Line Factory project at the Broad Institute, together with hospital partners, to explore how best to overcome current obstacles, produce faithful models at scale and explore the ethical issues associated with enabling rapid and unfettered access. Our project aims to:

Establish robust SOPs for major cancer types

Comprehensively characterize genomes of new models

Enable rapid and unfettered access

Prioritize treatments for each cancer patient

We've made exciting progress and are always looking for new collaborators.

The Connectivity Map (or CMap) is a catalog of gene-expression data collected from human cells treated with chemical compounds and genetic reagents. Computational methods to reduce the number of necessary genomic measurements along with streamlined methodologies enable the current effort to significantly increase the size of the CMap database and along with it, our potential to connect human diseases with the genes that underlie them and the drugs that treat them.

To gain insight into the genomic basis of diffuse large B-cell lymphoma (DLBCL), we performed massively parallel whole-exome sequencing of 55 primary tumor samples from patients with DLBCL and matched normal tissue. We identified recurrent mutations in genes that are likely to play a role in the biology of DLBCL.